Gathering data and applying context

A general rule concerning machine learning specifically, and automation techniques generally, is that they require large amounts of data to be effective. More data will enable the machine to make better decisions and solve more complex problems. Part of the data that can be gathered will help the machines apply context to what they are seeing. Currently, algorithms struggle with qualitative analysis. Algorithms that can tell you what happened using a large dataset are commodities at this point. This is not to say these algorithms are not helpful, they are simply common. Some algorithms are also predictive. With enough historical data, some algorithms have become good at predicting what will happen next. This is largely based on pattern recognition and determining the next logical data point given the historical data. People should be very careful with predictive algorithms because incomplete datasets can lead to poor predictions. Also, machines have...